Multitarget tracking with adaptive detection thresholds

The idea of adjusting the detection thresholds adaptively to enhance the performance of an overall tracking system has been one of the important areas studied in tracking community for the last ten years. However, most of the previous work was developed for single target environments where a simple algorithm such as nearest neighbor (NN) or probabilistic data association (PDA) filter was assumed to be used in the tracking system. We study the issues of adaptive detection thresholds based on the assumption that an optimal assignment algorithm is adopted for a multitarget and cluttered environment. This research is motivated by an important earlier work which makes the analytical evaluation of the optimal assignment algorithm possible. The performance measures considered for determining detection thresholds are the correct association probability and the expected estimation error. The analytical results obtained here represent the upper bound of the tracking performance and can be used for designing and evaluating a tracking system.